For comparison, we consider a baseline scheme which employs a two-stage estimator for addressing the optimization problem in (REF ): First, the alternating least square (ALS) algorithm [1]} is applied to fit the model in (REF ) and \(\bar{\mathbf {G}}_l=\mathbf {P}_l\mathbf {G}\)  is regarded unknown to estimate. As for the second stage, we compute the minimum \(l_1\) -norm solutions for the sparse factor matrices \(\mathbf {G}\)  from the estimation of \({\bar{\mathbf {G}}}_l\)  and use the existing convex
optimization solver [2]} to solve this least absolute shrinkage and selection operator (LASSO) problem. Since the number of active devices \(K_a\)  is unknown, we set the initial upper bounds of \(K_a\)  as \(K\)  in the two-stage estimator.
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